rezaeyvazpour Goto Github PK

arm-vo

Efficient monocular visual odometry for ground vehicles on ARM processors

awesome-point-clouds-registration

A list of papers about point clouds registration

eeglearn

A set of functions for supervised feature learning/classification of mental states from EEG based on "EEG images" idea.

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

matlab-orb-slam

A MATLAB implementation of ORB-SLAM using SURF features.

minimal-mistakes

:triangular_ruler: Jekyll theme for building a personal site, blog, project documentation, or portfolio.

mono-vo

An OpenCV based implementation of Monocular Visual Odometry

orb-slam2-gpu2016-final

rezaeyvazpour.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

siml

Machine Learning algorithms implemented from scratch

slam_resources

SLAM Resources to follow up current SLAM trends and papers.

svo2014

Unified Code-base for Official Non-ROS SVO2014 Repository.

verilog-hdl

things about Verilog hardware description language

visual-odometry-review

SLAM is mainly divided into two parts: the front end and the back end. The front end is the visual odometer (VO), which roughly estimates the motion of the camera based on the information of adjacent images and provides a good initial value for the back end.The implementation methods of VO can be divided into two categories according to whether features are extracted or not: feature point-based methods, and direct methods without feature points. VO based on feature points is stable and insensitive to illumination and dynamic objects

yuzhangbit.github.io

my website based on https://github.com/mmistakes/minimal-mistakes. Please visit https://yuzhangbit.github.io for details.